Update model card: new performance chart (#2864)

* Update model performance for correct German conll03 dataset

* Adjust text

* Adjust line spacing

model_cards/bert-base-german-cased-README.md

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 language: german
-thumbnail: https://thumb.tildacdn.com/tild3330-3735-4461-b133-656238643834/-/format/webp/deepset_performance.png
+thumbnail: https://thumb.tildacdn.com/tild3162-6462-4566-b663-376630376138/-/format/webp/Screenshot_from_2020.png
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 # German BERT
@@ -43,11 +43,11 @@ During training we monitored the loss and evaluated different model checkpoints
 - 10kGNAD: Accuracy for document classification
 
 Even without thorough hyperparameter tuning, we observed quite stable learning especially for our German model. Multiple restarts with different seeds produced quite similar results.
-![performancetable](https://thumb.tildacdn.com/tild3330-3735-4461-b133-656238643834/-/format/webp/deepset_performance.png)  
   
-While outperforming on 4 out of 5 task we wondered why the German BERT model did not outperform on CONLL03-de. So we compared English BERT with multilingual on CONLL03-en and found them to perform similar as well.
+![performancetable](https://thumb.tildacdn.com/tild3162-6462-4566-b663-376630376138/-/format/webp/Screenshot_from_2020.png)  
 
 We further evaluated different points during the 9 days of pre-training and were astonished how fast the model converges to the maximally reachable performance. We ran all 5 downstream tasks on 7 different model checkpoints - taken at 0 up to 840k training steps (x-axis in figure below). Most checkpoints are taken from early training where we expected most performance changes. Surprisingly, even a randomly initialized BERT can be trained only on labeled downstream datasets and reach good performance (blue line, GermEval 2018 Coarse task, 795 kB trainset size).
+
 ![checkpointseval](https://thumb.tildacdn.com/tild6335-3531-4137-b533-313365663435/-/format/webp/deepset_checkpoints.png)  
 
 ## Authors